The ECT method uses Backbone to use GoogleNet[1]} and ResNet101[2]} initialized by ImageNet pre-training, and the image size of the input network is adjusted to the size specified by the network input. In the synthetic virtual feature experiment of unseen class, the ridge regression \(\lambda \)  is set to 1.0, and the virtual feature part of the unseen class generated by the feature mean and standard deviation uses the random.normal function in the ‘Numpy package’. Use the regression algorithm in the ‘Sklearn package’ to select the classifier. The learning rate parameter of the Lasso classifier is 0.001, the alpha parameter of the ridge regression classifier is set to (0.1, 0.5, 1.0, 5.0, 10.0), and Bayesian uses classification. At the same time, the parameters used by the original LFGAA network have not been changed.
